Classification trees are nonparametric statistical learning methods that incorporate feature selection and interactions, possess intuitive interpretability, are efficient, and have high prediction accuracy when used in ensembles. This paper provides a brief introduction to the classification tree‐based methods, a review of the recent developments, and a survey of the applications in bioinformatics and statistical genetics. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 55‐63 DOI: 10.1002/widm.14

中文翻译：

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分类树在生物信息学中的应用。

分类树是一种非参数统计学习方法，它结合了特征选择和交互，具有直观的可解释性，效率高，在集成中使用时具有高预测精度。本文简要介绍了基于分类树的方法，回顾了最近的发展，并对生物信息学和统计遗传学中的应用进行了调查。© 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 55-63 DOI: 10.1002/widm.14